Overview

Dataset statistics

Number of variables17
Number of observations2680040
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory347.6 MiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical3

Alerts

geo_country has a high cardinality: 220 distinct values High cardinality
event_timestamp has a high cardinality: 1722186 distinct values High cardinality
mission_difficulty is highly correlated with mission_stars_collected and 1 other fieldsHigh correlation
mission_stars_collected is highly correlated with mission_difficulty and 4 other fieldsHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
lifetime_played_runs is highly correlated with mission_stars_collected and 3 other fieldsHigh correlation
max_run_distance is highly correlated with mission_stars_collected and 2 other fieldsHigh correlation
total_purchases_virtual is highly correlated with virtual_currency_balanceHigh correlation
total_ads_watched is highly correlated with mission_stars_collected and 2 other fieldsHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
virtual_currency_balance is highly correlated with total_purchases_virtualHigh correlation
mission_played is highly correlated with mission_difficulty and 4 other fieldsHigh correlation
mission_difficulty is highly correlated with mission_playedHigh correlation
mission_stars_collected is highly correlated with mission_playedHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
lifetime_played_runs is highly correlated with mission_playedHigh correlation
max_run_distance is highly correlated with mission_playedHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
mission_played is highly correlated with mission_difficulty and 3 other fieldsHigh correlation
mission_stars_collected is highly correlated with lifetime_played_runs and 1 other fieldsHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
lifetime_played_runs is highly correlated with mission_stars_collected and 1 other fieldsHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
mission_played is highly correlated with mission_stars_collected and 1 other fieldsHigh correlation
mission_id is highly correlated with mission_playedHigh correlation
mission_difficulty is highly correlated with mission_playedHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
max_run_distance is highly correlated with total_ads_watchedHigh correlation
total_ads_watched is highly correlated with max_run_distanceHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
mission_played is highly correlated with mission_id and 1 other fieldsHigh correlation
mission_stars_collected is highly skewed (γ1 = 158.6920513) Skewed
total_purchases_real is highly skewed (γ1 = 67.63920479) Skewed
virtual_currency_balance is highly skewed (γ1 = 78.74891214) Skewed
event_timestamp is uniformly distributed Uniform
target_max_day_played has 225886 (8.4%) zeros Zeros
day_auto_increment has 1683097 (62.8%) zeros Zeros
total_purchases_virtual has 1318046 (49.2%) zeros Zeros
total_ads_watched has 1395498 (52.1%) zeros Zeros
total_purchases_real has 2665246 (99.4%) zeros Zeros
days_played_in_month has 1817797 (67.8%) zeros Zeros

Reproduction

Analysis started2022-05-25 15:23:28.406530
Analysis finished2022-05-25 15:29:47.621821
Duration6 minutes and 19.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct268662
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151370.7859
Minimum0
Maximum290201
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:47.757458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13639
Q186348.75
median154225
Q3222213
95-th percentile276615
Maximum290201
Range290201
Interquartile range (IQR)135864.25

Descriptive statistics

Standard deviation82835.14281
Coefficient of variation (CV)0.5472333538
Kurtosis-1.096503108
Mean151370.7859
Median Absolute Deviation (MAD)67932
Skewness-0.1304178715
Sum4.056797609 × 1011
Variance6861660884
MonotonicityNot monotonic
2022-05-25T17:29:47.903070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7886810
 
< 0.1%
27462910
 
< 0.1%
25006410
 
< 0.1%
8731310
 
< 0.1%
15523410
 
< 0.1%
24857110
 
< 0.1%
14552710
 
< 0.1%
21947510
 
< 0.1%
25063510
 
< 0.1%
18375610
 
< 0.1%
Other values (268652)2679940
> 99.9%
ValueCountFrequency (%)
010
< 0.1%
110
< 0.1%
210
< 0.1%
310
< 0.1%
410
< 0.1%
510
< 0.1%
610
< 0.1%
710
< 0.1%
810
< 0.1%
910
< 0.1%
ValueCountFrequency (%)
29020110
< 0.1%
29020010
< 0.1%
29019910
< 0.1%
29019810
< 0.1%
29019710
< 0.1%
29019610
< 0.1%
29019510
< 0.1%
29019410
< 0.1%
29019310
< 0.1%
29019210
< 0.1%

user_pseudo_id
Real number (ℝ≥0)

Distinct268662
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49983770.56
Minimum794
Maximum99999617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:48.054665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum794
5-th percentile5015829
Q124965731
median50053499
Q374913508
95-th percentile94929788
Maximum99999617
Range99998823
Interquartile range (IQR)49947777

Descriptive statistics

Standard deviation28862679.47
Coefficient of variation (CV)0.5774410204
Kurtosis-1.201938662
Mean49983770.56
Median Absolute Deviation (MAD)24976003
Skewness-0.002183217932
Sum1.339585044 × 1014
Variance8.330542664 × 1014
MonotonicityIncreasing
2022-05-25T17:29:48.201272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79410
 
< 0.1%
6656673810
 
< 0.1%
6656679810
 
< 0.1%
6656784710
 
< 0.1%
6656794510
 
< 0.1%
6656867610
 
< 0.1%
6656877010
 
< 0.1%
6656899410
 
< 0.1%
6656941310
 
< 0.1%
6656994610
 
< 0.1%
Other values (268652)2679940
> 99.9%
ValueCountFrequency (%)
79410
< 0.1%
94310
< 0.1%
103010
< 0.1%
251110
< 0.1%
272310
< 0.1%
299110
< 0.1%
319410
< 0.1%
384210
< 0.1%
548010
< 0.1%
621310
< 0.1%
ValueCountFrequency (%)
9999961710
< 0.1%
9999943210
< 0.1%
9999933410
< 0.1%
9999870110
< 0.1%
9999836710
< 0.1%
9999824010
< 0.1%
9999786810
< 0.1%
9999762010
< 0.1%
9999731010
< 0.1%
9999645210
< 0.1%

target_max_day_played
Real number (ℝ≥0)

ZEROS

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.445219474
Minimum0
Maximum184
Zeros225886
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:48.344397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q38
95-th percentile21
Maximum184
Range184
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.95990996
Coefficient of variation (CV)1.235009916
Kurtosis21.18527688
Mean6.445219474
Median Absolute Deviation (MAD)3
Skewness3.559841456
Sum17273446
Variance63.36016657
MonotonicityNot monotonic
2022-05-25T17:29:48.480035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1347321
13.0%
2339491
12.7%
3293519
11.0%
4241931
9.0%
0225886
8.4%
5199386
 
7.4%
6164302
 
6.1%
7132438
 
4.9%
8109220
 
4.1%
990444
 
3.4%
Other values (101)536102
20.0%
ValueCountFrequency (%)
0225886
8.4%
1347321
13.0%
2339491
12.7%
3293519
11.0%
4241931
9.0%
5199386
7.4%
6164302
6.1%
7132438
 
4.9%
8109220
 
4.1%
990444
 
3.4%
ValueCountFrequency (%)
18410
< 0.1%
15910
< 0.1%
15110
< 0.1%
14110
< 0.1%
12810
< 0.1%
10710
< 0.1%
10620
< 0.1%
10320
< 0.1%
10220
< 0.1%
10120
< 0.1%

mission_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.2186986
Minimum1
Maximum372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:48.624156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median66
Q3113
95-th percentile115
Maximum372
Range371
Interquartile range (IQR)102

Descriptive statistics

Standard deviation49.15026032
Coefficient of variation (CV)0.829978731
Kurtosis-1.797156115
Mean59.2186986
Median Absolute Deviation (MAD)48
Skewness0.04218996138
Sum158708481
Variance2415.748089
MonotonicityNot monotonic
2022-05-25T17:29:48.921441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115278002
10.4%
3277925
10.4%
86269273
10.0%
114259776
9.7%
19259288
9.7%
11258687
9.7%
113255342
9.5%
109254863
9.5%
6248149
9.3%
12187138
7.0%
Other values (213)131597
4.9%
ValueCountFrequency (%)
15
 
< 0.1%
3277925
10.4%
590
 
< 0.1%
6248149
9.3%
7602
 
< 0.1%
820
 
< 0.1%
9100
 
< 0.1%
1077412
 
2.9%
11258687
9.7%
12187138
7.0%
ValueCountFrequency (%)
3722
 
< 0.1%
3711
 
< 0.1%
3704
< 0.1%
3696
< 0.1%
3671
 
< 0.1%
3663
 
< 0.1%
3645
< 0.1%
3638
< 0.1%
3611
 
< 0.1%
3607
< 0.1%

mission_difficulty
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 MiB
1.0
1933528 
2.0
746492 
3.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01933528
72.1%
2.0746492
 
27.9%
3.020
 
< 0.1%

Length

2022-05-25T17:29:49.050018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-25T17:29:49.117836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.01933528
72.1%
2.0746492
 
27.9%
3.020
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mission_stars_collected
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.50493724
Minimum0
Maximum1772
Zeros5959
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:49.211586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median7
Q311
95-th percentile14
Maximum1772
Range1772
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.042256999
Coefficient of variation (CV)0.6718586495
Kurtosis55099.91193
Mean7.50493724
Median Absolute Deviation (MAD)3
Skewness158.6920513
Sum20113532
Variance25.42435564
MonotonicityNot monotonic
2022-05-25T17:29:49.348220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3483211
18.0%
6276592
10.3%
8259368
9.7%
4232340
8.7%
7214456
8.0%
5206933
7.7%
11192766
 
7.2%
14176941
 
6.6%
9175642
 
6.6%
12130291
 
4.9%
Other values (68)331500
12.4%
ValueCountFrequency (%)
05959
 
0.2%
116357
 
0.6%
26960
 
0.3%
3483211
18.0%
4232340
8.7%
5206933
7.7%
6276592
10.3%
7214456
8.0%
8259368
9.7%
9175642
 
6.6%
ValueCountFrequency (%)
17721
< 0.1%
17692
< 0.1%
17681
< 0.1%
17661
< 0.1%
17651
< 0.1%
17612
< 0.1%
17601
< 0.1%
17591
< 0.1%
2801
< 0.1%
2771
< 0.1%

day_auto_increment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6727026462
Minimum0
Maximum58
Zeros1683097
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:49.488844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum58
Range58
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.283770145
Coefficient of variation (CV)1.908376832
Kurtosis44.35873336
Mean0.6727026462
Median Absolute Deviation (MAD)0
Skewness4.363842316
Sum1802870
Variance1.648065786
MonotonicityNot monotonic
2022-05-25T17:29:49.618498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01683097
62.8%
1611625
 
22.8%
2203464
 
7.6%
386629
 
3.2%
441820
 
1.6%
522351
 
0.8%
611932
 
0.4%
76907
 
0.3%
84131
 
0.2%
92660
 
0.1%
Other values (40)5424
 
0.2%
ValueCountFrequency (%)
01683097
62.8%
1611625
 
22.8%
2203464
 
7.6%
386629
 
3.2%
441820
 
1.6%
522351
 
0.8%
611932
 
0.4%
76907
 
0.3%
84131
 
0.2%
92660
 
0.1%
ValueCountFrequency (%)
581
 
< 0.1%
521
 
< 0.1%
511
 
< 0.1%
506
< 0.1%
461
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
421
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%

lifetime_played_runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct285
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.16777772
Minimum0
Maximum632
Zeros5769
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:49.758124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile19
Maximum632
Range632
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.930375263
Coefficient of variation (CV)1.28577514
Kurtosis184.8159278
Mean6.16777772
Median Absolute Deviation (MAD)2
Skewness7.62512371
Sum16529891
Variance62.8908518
MonotonicityNot monotonic
2022-05-25T17:29:49.888283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2542669
20.2%
1417848
15.6%
3328215
12.2%
4237218
8.9%
5190534
 
7.1%
6158236
 
5.9%
7128681
 
4.8%
8105395
 
3.9%
984058
 
3.1%
1068743
 
2.6%
Other values (275)418443
15.6%
ValueCountFrequency (%)
05769
 
0.2%
1417848
15.6%
2542669
20.2%
3328215
12.2%
4237218
8.9%
5190534
 
7.1%
6158236
 
5.9%
7128681
 
4.8%
8105395
 
3.9%
984058
 
3.1%
ValueCountFrequency (%)
6321
 
< 0.1%
45410
< 0.1%
4081
 
< 0.1%
4061
 
< 0.1%
4031
 
< 0.1%
4023
 
< 0.1%
3891
 
< 0.1%
3872
 
< 0.1%
3862
 
< 0.1%
3853
 
< 0.1%

max_run_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9672
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2385.666054
Minimum0
Maximum28463
Zeros5795
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:50.024918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1140
Q11501
median2134
Q32905
95-th percentile4628
Maximum28463
Range28463
Interquartile range (IQR)1404

Descriptive statistics

Standard deviation1189.046409
Coefficient of variation (CV)0.4984127629
Kurtosis7.483256069
Mean2385.666054
Median Absolute Deviation (MAD)677
Skewness1.876945208
Sum6393680451
Variance1413831.364
MonotonicityNot monotonic
2022-05-25T17:29:50.158573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05795
 
0.2%
12241710
 
0.1%
11451707
 
0.1%
11601699
 
0.1%
11671696
 
0.1%
12011694
 
0.1%
13111691
 
0.1%
12341682
 
0.1%
11811679
 
0.1%
13121673
 
0.1%
Other values (9662)2659014
99.2%
ValueCountFrequency (%)
05795
0.2%
1031
 
< 0.1%
1043
 
< 0.1%
1082
 
< 0.1%
1132
 
< 0.1%
1172
 
< 0.1%
1183
 
< 0.1%
1191
 
< 0.1%
1222
 
< 0.1%
1233
 
< 0.1%
ValueCountFrequency (%)
284631
 
< 0.1%
2464810
< 0.1%
223714
 
< 0.1%
217441
 
< 0.1%
208074
 
< 0.1%
201532
 
< 0.1%
200533
 
< 0.1%
199621
 
< 0.1%
189152
 
< 0.1%
188763
 
< 0.1%

total_purchases_virtual
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct5242
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33653.92234
Minimum0
Maximum8099500
Zeros1318046
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:50.306684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500
Q34000
95-th percentile11000
Maximum8099500
Range8099500
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation368409.9334
Coefficient of variation (CV)10.94701324
Kurtosis165.7536268
Mean33653.92234
Median Absolute Deviation (MAD)500
Skewness12.64392757
Sum9.019385802 × 1010
Variance1.35725879 × 1011
MonotonicityNot monotonic
2022-05-25T17:29:50.447308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01318046
49.2%
1500314269
 
11.7%
4000127917
 
4.8%
550087067
 
3.2%
300080291
 
3.0%
50065517
 
2.4%
450064209
 
2.4%
600058088
 
2.2%
100048538
 
1.8%
200045424
 
1.7%
Other values (5232)470674
 
17.6%
ValueCountFrequency (%)
01318046
49.2%
50065517
 
2.4%
100048538
 
1.8%
1500314269
 
11.7%
200045424
 
1.7%
250037633
 
1.4%
300080291
 
3.0%
350023910
 
0.9%
4000127917
 
4.8%
450064209
 
2.4%
ValueCountFrequency (%)
80995001
< 0.1%
80985001
< 0.1%
80810001
< 0.1%
80120001
< 0.1%
80090001
< 0.1%
79280001
< 0.1%
79270001
< 0.1%
79225001
< 0.1%
79215001
< 0.1%
79205001
< 0.1%

total_ads_watched
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.431097297
Minimum0
Maximum239
Zeros1395498
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:50.592919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum239
Range239
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.999152451
Coefficient of variation (CV)2.056335819
Kurtosis82.71175618
Mean2.431097297
Median Absolute Deviation (MAD)0
Skewness5.938305411
Sum6515438
Variance24.99152523
MonotonicityNot monotonic
2022-05-25T17:29:50.727559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01395498
52.1%
1319677
 
11.9%
2217009
 
8.1%
3161255
 
6.0%
4122364
 
4.6%
593120
 
3.5%
670813
 
2.6%
754818
 
2.0%
842215
 
1.6%
933258
 
1.2%
Other values (160)170013
 
6.3%
ValueCountFrequency (%)
01395498
52.1%
1319677
 
11.9%
2217009
 
8.1%
3161255
 
6.0%
4122364
 
4.6%
593120
 
3.5%
670813
 
2.6%
754818
 
2.0%
842215
 
1.6%
933258
 
1.2%
ValueCountFrequency (%)
2391
 
< 0.1%
2351
 
< 0.1%
2301
 
< 0.1%
2251
 
< 0.1%
2201
 
< 0.1%
2171
 
< 0.1%
2051
 
< 0.1%
2021
 
< 0.1%
2013
< 0.1%
1981
 
< 0.1%

total_purchases_real
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct605
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02279883509
Minimum0
Maximum133.93
Zeros2665246
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:50.866188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum133.93
Range133.93
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6211283974
Coefficient of variation (CV)27.24386553
Kurtosis6996.434685
Mean0.02279883509
Median Absolute Deviation (MAD)0
Skewness67.63920479
Sum61101.79
Variance0.3858004861
MonotonicityNot monotonic
2022-05-25T17:29:51.098595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02665246
99.4%
1.992135
 
0.1%
0.481147
 
< 0.1%
0.49813
 
< 0.1%
4.98661
 
< 0.1%
0.99598
 
< 0.1%
0.37440
 
< 0.1%
2.99420
 
< 0.1%
3.99356
 
< 0.1%
0.11261
 
< 0.1%
Other values (595)7963
 
0.3%
ValueCountFrequency (%)
02665246
99.4%
0.11261
 
< 0.1%
0.135
 
< 0.1%
0.1425
 
< 0.1%
0.2271
 
< 0.1%
0.272
 
< 0.1%
0.31
 
< 0.1%
0.3148
 
< 0.1%
0.3229
 
< 0.1%
0.33155
 
< 0.1%
ValueCountFrequency (%)
133.931
 
< 0.1%
96.694
 
< 0.1%
94.71
 
< 0.1%
92.533
 
< 0.1%
79.916
< 0.1%
78.764
 
< 0.1%
73.943
 
< 0.1%
73.9310
< 0.1%
67.8310
< 0.1%
64.976
< 0.1%

geo_country
Categorical

HIGH CARDINALITY

Distinct220
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 MiB
United States
449106 
Mexico
341118 
Brazil
 
131641
France
 
112166
Russia
 
107711
Other values (215)
1538298 

Length

Max length24
Median length7
Mean length8.140946404
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
United States449106
16.8%
Mexico341118
 
12.7%
Brazil131641
 
4.9%
France112166
 
4.2%
Russia107711
 
4.0%
India96080
 
3.6%
Germany91052
 
3.4%
Turkey82896
 
3.1%
United Kingdom82838
 
3.1%
Colombia68028
 
2.5%
Other values (210)1117404
41.7%

Length

2022-05-25T17:29:51.240216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united538218
16.2%
states449106
 
13.5%
mexico341118
 
10.3%
brazil131641
 
4.0%
france112166
 
3.4%
russia107711
 
3.2%
india96080
 
2.9%
germany91052
 
2.7%
turkey82896
 
2.5%
kingdom82838
 
2.5%
Other values (249)1281760
38.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

days_played_in_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4930090596
Minimum0
Maximum25
Zeros1817797
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:51.356905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum25
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9290332314
Coefficient of variation (CV)1.884414116
Kurtosis18.69182895
Mean0.4930090596
Median Absolute Deviation (MAD)0
Skewness3.227741826
Sum1321284
Variance0.863102745
MonotonicityNot monotonic
2022-05-25T17:29:51.469604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
01817797
67.8%
1592554
 
22.1%
2165924
 
6.2%
359195
 
2.2%
424085
 
0.9%
510830
 
0.4%
64801
 
0.2%
72282
 
0.1%
81160
 
< 0.1%
9617
 
< 0.1%
Other values (16)795
 
< 0.1%
ValueCountFrequency (%)
01817797
67.8%
1592554
 
22.1%
2165924
 
6.2%
359195
 
2.2%
424085
 
0.9%
510830
 
0.4%
64801
 
0.2%
72282
 
0.1%
81160
 
< 0.1%
9617
 
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
243
 
< 0.1%
233
 
< 0.1%
222
 
< 0.1%
211
 
< 0.1%
205
 
< 0.1%
193
 
< 0.1%
187
< 0.1%
1713
< 0.1%
1611
< 0.1%

virtual_currency_balance
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct46605
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328729.3675
Minimum0
Maximum2146000000
Zeros200
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:51.605241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile605
Q14017
median5648
Q36987
95-th percentile11442
Maximum2146000000
Range2146000000
Interquartile range (IQR)2970

Descriptive statistics

Standard deviation16969510.17
Coefficient of variation (CV)51.62152166
Kurtosis7010.745718
Mean328729.3675
Median Absolute Deviation (MAD)1523
Skewness78.74891214
Sum8.810078542 × 1011
Variance2.879642753 × 1014
MonotonicityNot monotonic
2022-05-25T17:29:51.737886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55791858
 
0.1%
55701841
 
0.1%
55541827
 
0.1%
55451801
 
0.1%
55711789
 
0.1%
55741788
 
0.1%
55591767
 
0.1%
55621766
 
0.1%
55661765
 
0.1%
55481755
 
0.1%
Other values (46595)2662083
99.3%
ValueCountFrequency (%)
0200
< 0.1%
1166
< 0.1%
2183
< 0.1%
3173
< 0.1%
4175
< 0.1%
5181
< 0.1%
6156
< 0.1%
7178
< 0.1%
8162
< 0.1%
9154
< 0.1%
ValueCountFrequency (%)
21460000009
< 0.1%
20000000004
 
< 0.1%
19999900001
 
< 0.1%
19976500002
 
< 0.1%
19961500003
 
< 0.1%
19961400005
< 0.1%
199562000010
< 0.1%
199525000010
< 0.1%
19941100005
< 0.1%
19941000008
< 0.1%

event_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1722186
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size20.4 MiB
03-02-2022 15:34:43
 
10
22-02-2022 20:34:24
 
10
04-02-2022 23:45:10
 
9
12-02-2022 18:03:09
 
9
27-02-2022 16:13:14
 
9
Other values (1722181)
2679993 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1062528 ?
Unique (%)39.6%

Sample

1st row19-02-2022 18:47:18
2nd row19-02-2022 18:47:52
3rd row19-02-2022 18:47:57
4th row19-02-2022 18:51:30
5th row19-02-2022 18:53:40

Common Values

ValueCountFrequency (%)
03-02-2022 15:34:4310
 
< 0.1%
22-02-2022 20:34:2410
 
< 0.1%
04-02-2022 23:45:109
 
< 0.1%
12-02-2022 18:03:099
 
< 0.1%
27-02-2022 16:13:149
 
< 0.1%
11-02-2022 16:00:179
 
< 0.1%
10-02-2022 18:03:119
 
< 0.1%
22-02-2022 17:11:119
 
< 0.1%
27-02-2022 20:39:339
 
< 0.1%
10-02-2022 02:28:209
 
< 0.1%
Other values (1722176)2679948
> 99.9%

Length

2022-05-25T17:29:51.929375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27-02-2022102729
 
1.9%
26-02-2022101032
 
1.9%
20-02-2022100948
 
1.9%
25-02-202297505
 
1.8%
19-02-202296787
 
1.8%
22-02-202296127
 
1.8%
21-02-202294737
 
1.8%
13-02-202294730
 
1.8%
06-02-202294083
 
1.8%
12-02-202293916
 
1.8%
Other values (86499)4387486
81.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mission_played
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.496324309
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 MiB
2022-05-25T17:29:52.025118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.87192445
Coefficient of variation (CV)0.522517284
Kurtosis-1.224021514
Mean5.496324309
Median Absolute Deviation (MAD)2
Skewness0.001494280985
Sum14730369
Variance8.247950045
MonotonicityNot monotonic
2022-05-25T17:29:52.115875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1268437
10.0%
2268384
10.0%
3268319
10.0%
4268208
10.0%
5268123
10.0%
6268033
10.0%
7267886
10.0%
8267762
10.0%
9267616
10.0%
10267272
10.0%
ValueCountFrequency (%)
1268437
10.0%
2268384
10.0%
3268319
10.0%
4268208
10.0%
5268123
10.0%
6268033
10.0%
7267886
10.0%
8267762
10.0%
9267616
10.0%
10267272
10.0%
ValueCountFrequency (%)
10267272
10.0%
9267616
10.0%
8267762
10.0%
7267886
10.0%
6268033
10.0%
5268123
10.0%
4268208
10.0%
3268319
10.0%
2268384
10.0%
1268437
10.0%

Interactions

2022-05-25T17:29:23.734316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:40.273084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:56.022767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:11.562966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:28.084119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:07.664919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:23.229643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:37.952024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:53.380086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:09.176141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:24.477607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:39.589965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:53.851069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:08.618361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:24.631916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:41.216083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:56.905423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:12.452588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:30.698169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:08.585476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:24.094332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:38.858613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:54.310121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:10.052823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:25.371741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:40.445185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:54.711276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:09.513995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:25.507605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:42.106224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:57.784074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:13.302332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:33.322707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:09.497559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:24.940088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:39.745748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:55.216205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:10.918016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:26.240923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:41.311454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:55.550034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:10.388656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:27.218537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:43.866545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:59.505501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:15.104529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:36.601999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:11.214967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:26.600169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:41.462174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:56.960584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:12.603530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:28.096013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:42.939040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:57.213615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:12.091624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:30.246516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:46.825172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:02.434220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:18.086597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:41.359357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:14.190618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:29.513407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:44.500114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:59.920782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:15.539728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:31.020719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:45.758545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:00.093983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:15.001904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:31.147121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:47.744742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:03.363244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:18.982204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:44.001826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:15.080255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:30.336233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:45.391755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:00.846280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:16.398445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:31.868958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:46.558420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:00.934256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:15.871157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:32.034780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:48.650334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:04.296768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:19.909738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:46.709663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:15.968905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:31.188968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:46.257467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:01.769825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:17.315020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:32.777529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:47.437088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:01.762067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:16.732802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:32.959325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:49.602805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:05.214315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:20.823324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:49.389516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:16.891971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:32.046210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:47.164564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:02.681402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:18.237569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:33.641234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:48.247932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:02.622784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:17.623034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:33.835981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:50.509887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:06.107939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:21.922499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:52.274866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:17.771143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:32.863051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:48.047734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:03.625386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:19.164187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:34.489507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:49.031867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:03.444109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:18.474775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:34.739073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:51.432465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:07.020524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:22.822107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:54.998614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:18.674728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:33.708298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:48.933870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:04.554420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:20.046846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:35.331274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:49.838218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:04.283388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:19.344486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:35.619245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:52.333085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:07.921622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:23.688312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:57.639612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:19.548899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:34.534609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:49.799078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:05.566730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:20.911057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:36.169569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:50.597709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:05.098716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:20.204707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:36.517860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:53.248156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:08.841684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:24.579942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:00.386800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:20.451991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:35.386854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:50.685745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:06.485275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:21.774280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:37.025299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:51.396585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:05.934003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:21.081885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:37.393548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:54.170213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:09.742289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:25.466583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:03.086105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:21.459322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:36.201675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:51.549952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:07.383395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:22.657932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:37.860067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:52.175027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:06.756312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:21.932119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:38.286680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:25:55.115193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:10.685805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:26:26.351737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:05.884664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:22.371402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:37.064888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:27:52.458549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:08.328393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:23.588477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:38.752696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:28:52.993838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:07.627490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-25T17:29:22.834215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-25T17:29:52.228574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-25T17:29:52.490873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-25T17:29:52.745193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-25T17:29:53.011482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-25T17:29:38.842212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-25T17:29:41.032878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexuser_pseudo_idtarget_max_day_playedmission_idmission_difficultymission_stars_collectedday_auto_incrementlifetime_played_runsmax_run_distancetotal_purchases_virtualtotal_ads_watchedtotal_purchases_realgeo_countrydays_played_in_monthvirtual_currency_balanceevent_timestampmission_played
07886879401151.03.00.01.01183.00.00.00.0India0.05715.019-02-2022 18:47:181
17886879401131.03.00.01.01183.00.00.00.0India0.05715.019-02-2022 18:47:522
27886879401091.03.00.01.01183.00.00.00.0India0.05715.019-02-2022 18:47:573
3788687940111.06.00.03.01695.00.02.00.0India0.06842.019-02-2022 18:51:304
47886879401141.07.00.04.01695.00.03.00.0India0.07342.019-02-2022 18:53:405
578868794031.07.00.04.01695.00.03.00.0India0.07342.019-02-2022 18:54:106
6788687940192.09.00.05.02104.00.05.00.0India0.08284.019-02-2022 18:59:127
7788687940862.011.00.06.02104.00.06.00.0India0.08941.019-02-2022 19:00:138
8788687940122.011.00.06.02104.00.06.00.0India0.08941.019-02-2022 19:00:209
978868794061.015.00.08.02104.06000.08.00.0India0.03849.020-02-2022 04:33:1410

Last rows

df_indexuser_pseudo_idtarget_max_day_playedmission_idmission_difficultymission_stars_collectedday_auto_incrementlifetime_played_runsmax_run_distancetotal_purchases_virtualtotal_ads_watchedtotal_purchases_realgeo_countrydays_played_in_monthvirtual_currency_balanceevent_timestampmission_played
26800301295819999961721131.03.01.01.01498.00.00.00.0Mexico1.05563.012-02-2022 14:58:381
26800311295819999961721091.03.01.01.01498.00.00.00.0Mexico1.05563.012-02-2022 14:58:552
268003212958199999617231.05.01.02.01498.00.00.00.0Mexico1.05632.012-02-2022 15:00:373
2680033129581999996172111.05.01.02.01498.00.00.00.0Mexico1.05632.012-02-2022 15:01:114
26800341295819999961721141.07.01.03.01888.01500.00.00.0Mexico1.05368.012-02-2022 15:02:365
26800351295819999961721151.07.01.03.01888.01500.00.00.0Mexico1.05368.012-02-2022 15:02:466
268003612958199999617261.07.01.03.01888.01500.00.00.0Mexico1.05368.012-02-2022 15:03:077
2680037129581999996172192.010.01.04.01888.04000.00.00.0Mexico1.03616.012-02-2022 15:04:288
2680038129581999996172122.012.01.05.01888.04000.00.00.0Mexico1.03616.012-02-2022 15:05:369
2680039129581999996172862.014.01.06.01888.04000.00.00.0Mexico1.04463.012-02-2022 15:09:4610